IoT Leaders
IoT Leaders

Episode · 1 year ago

Maximising Asset Value: The Importance of Data & How to Use it Properly


Many companies are under-utilizing their data—a misstep caused by misunderstanding the value of the asset, how to properly mine the information, or how to blend the tacit knowledge w/ the explicit data.

Dr. Satyam Priyadarshy, Technology Fellow and Chief Data Scientist at Halliburton, joins the show to discuss his opinions and strategies regarding data and how companies can take advantage of the bulk of their assets.

What we talked about:

  • Making Sense & Insight Around Data
  • The Importance of Historical Data
  • Strategies for Mining Data within a Company
  • The Application of Artificial Intelligence
  • Hiring Different Types of People at Halliburton

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You're listening to Iot leaders, a podcast from Si that shares real IOT stories from the field about digital transformation, swings and Mrs Lessons Learned in Innovation Strategies that work. In each episode, you'll hear our conversations with top digitization leaders on how iote is changing the world for the better. Let iot leaders be your guide to Iot digital transformation in innovation. Let's get into the show. Welcome to the latest episode of Iot leaders, the podcast that aims to demistify the complex, intriguing world of iote. My Name's Nicole, your host, and I'm the CEO of Si Global Iote Company, and today I'm delighted to have as my guest on Iot leaders Dr Sachian Priya dashy and such a works for Halliburton and his title is a chief data scientist and he's also a technology fellow at Halliburton. And if that's not enough, he's involved in several startups and is a senior fellow at a judge Mason University on Cyber Security, as well as a nut jung professor at Georgetown University. So definitely a busy man and such, I am very, very welcome to iote leaders podcast. Thank you, nick. Thanks for the invite. Certainly yes, or a little bit addition to the profile. I'm not no longer with Georgetown, but I moved on and I'm now with Virginia attacked, and Oklahoma State and university in India. So it's just like what happened to in as a farmer. Academics you never leave academics, you see. Yeah, yeah, it's well, I don't know how you find time for a little bit, but thank you again. I'm time for this half an hour, so that we've probably been been together today. So such a it's a big subject and I never we spoke prior to this. It's a pretty wide ranging subject as well, so it's trying to break it down into two pieces. Maybe I can just ask you have paps, just a little bit about your own background before we be died in. How what was your journey like? How did you, how didn't you get to where you are today? Being cheap taking scientists for Haliburton and perhaps for those people who don't know Howiberton because most people do what they will be people who do maybe just a little bit overview of what Haliburton does in the ord gas bunch. So first of all, let's talk about Haliburton, then I'll talk about my journey. Haliburton is hundred and two years old company. It's one of the world's largest energy services company. I think one of the first pattern the company fild was in areas of cementing by Mr Halliburton, and that's the company named after him and it is over the over the decades and a hundred years, it has gone through a lot of expansion in different fields, but primarily it is a body call is company which actually collaborates, an engineer solutions to maximize the asset value. Now it's a very, very important part to remember. It's about maximizing the air set value. You know, in olden days we would just call the hydrocarbon as an asset, but in today's world data becomes another asset. So that's just works in the ground in the efficient that the dayser is increasing to be instance, which is absolute. We're going to go right, absolutely so. And as at the company is Global. We have for about Fiftyzero people around the world, pretty much representing most nationalities in the world. And of course the challenging task of brilliant completion exploration. All are all are very complex scientific and engineering based.

A lot of highly skilled people are in the company as well. So it's a great place in that sense because you get to interact with mathematics engineer, scientist, physics geophysist. If you look at the spectrum of talent that is there in the company, is significant and you call my own journey. So I did my PhD in quantum mechanics apply to biophysics when I was pretty young and and pretty concept of how do if you think in a very simple terms, you know solar selves we make an efficiency of fifteen percent to twenty five percent, but nature makes solar cells with almost hundred percent efficiency. So my PhD was like trying to understand what is going on inside a chloroplast from a quantum mechanical point of view. But I think I was way too ahead of the curve because it just can't compute anything, because it involved a computation of five thousand by a five thousand metrics and an and on. Sparse metrics you can compute easily because there are no super computers then an even today. Not Sparse metrics the challenge. And then I switched on and I was trained by a advisor. I think I owe my success to him a lot because he trained me in such a way that you think of the problem not as single problem. It's a multiple problem issue and you should always be open to addressing multiple problems at the same time and as a result. In fact, during my PhD he said not to do work on he won't give me these PhD unless I publish more than topic. You know. So, which which is really good because it opens up your mind to different problems. And so I went and switched career and not carriers. I went to POSTDOC. Liked to postdocs in Australia in a totally different field, a glassy dynamics and lipid membranes, and then I came to us and did work in DNA electron transfer and non linear optics and many other topics. So we have a very special model called Barton and pre Othershi model for DNA electron transfer, you know. But then I did a lot of super computing. But if you think our foundation is all in data, will being quantum mechanist to generate a lot of data, and here I was the generator of the data as well as analyze, analyzing the data for sign very complex problems. And so I switched my careers from there to become a technologist at a company called AOL, which is America Online. That brought Internet bombs and Lucky I was lucky there that seven years I went from a individual contributor to becoming head of research in two thousand and five two and set up a ice and perfectcellence in two thousand and five in Beijing and Bangalore, and then I did my mba during that time and switch my careers again to become an executive turnaround. Some companies got involved in startups and, as a result, one day I got a call from Haliburton or a recruiter from while in gas industry set they're looking for someone like this and with its experience. I am having fun for last seven years in Ali Burton, when I set up my center of excellence for a big data, data, signs and digital in twenty four, when nobody was thinking about it in the industry. Well, there's enough material. Leaver about twenty podcasts from flora plus and to Hollyburton and big data. So so let's let's just pick that last subject and go deep, because you said they're instantly. He said the pink nobody was thinking about it while I go in the subject the Biot, of course a lot of people say, well, I actually product for Myot is the data, although having it would seem to be that in certainly in the field that you're in, there's almost there's no shortage of data. They may well be a shortage insight as to what it actually means, but it...

...seems like the amount of data that we're nowt technically able to create it. I mean it's not a dripping tame or even running to tap it. It's not leave it the host pipe that it appears to be almost like it's soun army, and it's only going to get more and more and more. I guess your work must rotate around that in terms of how on earth can make sense and insight and interpret this data that is coming out as in the context of the business value of the data. It's absolutely so. You know, people can challenge them on my comment that people are not thinking about it, but that's not that is to an extent true. But then dustry have been general rating data, no doubt about it. Right and but if you look talk to experts, they will say that or even today this is we're talking about twenty twenty one people will say or data is of not good quality, data is not complete. So if you think of where the world is an if you'll look at data native companies, which Ewel was one of them, and now the googles of the world, you can call them now, if they also had the same problem of data not complete and a bad quality, then they would not be making money, right, because the money comes by analyzing the data, building recommendation, inn whatever it is. Now, if, in an engineering form, we keep saying the data is of not good quality, then what have we done to fix that right? And that's where I think will come back to more little details later. I think the IOTI is or the industrial iot will really make sense because that can actually improve the quality of the data. Right now, industry collected the data, and no doubt and the significant amount from exploration to drilling and completion and H S and other areas. But if you look at it, the data was collected in the field. It was transmitted to the back office at a certain frequency. So not all the data was ever moved back office, but I if it was moved, it was moved in a later stage, so to say. So there was no real time so to say, analysis of it to the extent that it could have been done. Now, sir, if you look at even the last twenty years evolution of the technology, for example, such technology like we all depend on it right, for all our practical life. We depend on it. But when it comes to implementation in the energy sector, that has been really poored. If you go to any of the big energy companies and you want to search for him, say, a rotatory pump that you have used for last ten years and you have repaired it twenty times, and if you want to search for that repair log you will not find it. Why? Why? We hear these stories a lot about it's almost a paradox in a way that the companies that arguably could benefit the most from the data, like this device has broken twenty two times on the run. It's not broken for the twenty first time. Can I just find out what to do? And that would say me a lot of money. And there's so much money involved, and oil expiation, for example, is a massively capital intensive, it's very expensive business. It's all about classing. The time you hear these stories and then you also hear not just what a better but large companies in general often are not the ones who have captured the learnings, captured the knowledge, and they are just some extent behind. Why is that? Is it a cultural thing? Is it? Is it do you believe? Is it a volume of data thing? Is it that they were just focusing on something else as a priority, like finding the the Oily Gash? What do you think that is? So I think my might take on that is that, you know, while this technology evolved, the industry did not spend any in like did not spend well on actually understanding these technology. Well, right, so, whoever, a vender came and said, Oh,... is my search platform, you can implement it to search your corporate website. Yeah, and that's of God implemented. Nobody looked at the use case, how it could be really will be done for other areas or on top of a database. Right, so those two are not thought about very well. That how how the value will come and also one of the culture. It's a kind of a cultural thing and call it a business strategy. When you are in an operational mode, your goal is to fix the problem and move on. Yes, but if you look at the whole framework of data science, is that you want to do? The science on the data. That means you, your historical data is your really good source to do that science experiment right, because you already broke your pomp, already broke Ye. You have been of the data. Now I can actually analyze that data and understand that for last five times, why did it break, so that I can avoid those situations or those conditions or can address those conditions well in Ad Mans before it happens the next time. So the thought process has to be there. That historical data has significant value and that value could be measured. And in fact, when we did some experiments, I would call them experiments, in early two thousand and fourteen, we showed that how much valued is there in terms of monetary value? That the that the organization saw their come. Industry has lost. So the sitting said, yeah, sorry, they're sitting on an ass at the huge assay which is historical days a but in general people are looking in the direction for the value developing in the other direction, all the new things the channey needs or what they're doing next, new implications, but actually lot of value in the legacy. Days it's just sitting run split and I'm sure in your interaction you must have come to people asking the question who owns the data? Yes, right now, if you think of as a business and you're being a CEO. So if in principle, the company owns that data, yeah, right. So why is this question about who owns the data within a company? So when you start thinking that the data is owned by the company, then you start analyzing it and that means people should start collaborating. In fact, I wrote an article two thousand and fifteen, I think, called data democratization, and initially there was a pushback thing. We can't talk about these things. But democratization doesn't mean to give away. And how can we look at that? Need that idea. And and you're in a workflow. You have US three steps Moret of data, somebody has another to next three steps work of data. When you look at the whole workflow holistically, then only you can create dvial when you're doing things in a silo, you really can't create value too much right. It's a finely. Excuse me, such a help. I mean this value I think knowlergy have got my mind, is that was gold under our feet. We just can't get hold of it. But there there's value in the data. This huge amounts of data. It's owned by the company, but but there it's not like there's any issues in accessing the there. It's already their data. So where do you start? It seems like what you're addressing, your role in Halliburton, is so very big problem. But it put a very important problem in the very valuable problem to solve. But it's also a very broad problem. I mean there's data everywhere. It's it's everywhere and there's Timor creating it every increasing values of what. What advice would you give on the people who past listening to this? Think you will. Yeah, Michem is in the same situation and we got all this data, we got all this new data. Without getting all this needs technology and sensus. How do you go about getting strategy to mind the value? Yeah, so you see what I have been doing in for last seven years. I'll say from that...

...practice and I've been done this for other industry before, so I know that it works. You see, use if one is you have as a CEO or as a people be under CEEO. People already know where the serious problems are at. It Act at a tacit knowledge. They may not have a quantification of it precisely because it's a good idea exactly. Otherwise they wouldn't be in that seat. Yeah, so you look at the experts saying, okay, in the last five years, six years, what thing could have been really improved? And then you break down the problem into what it called the sprints, as you call your and you want to run that marathon, but you want to break down in sprints and saying, okay, I will take let's assume, take this example that I started with a fair rotatory pump. Right, if you are the if you have deployed that pump for twenty times and it has broken ar say at a at a rough asset knowledge perspective, it has broken after every three months of deployment. Yeah, you have some idea that it breaks after three months. Now I want to really narrow down that problem. Why does it happen? So you come up with them, you come up with a solution in a way that I can look at the data close to the three month period, five days before, at ten days before, a twenty days before, and see what happened really in that operation. So you basically what you do is you take a business problem which is you can say I will say fifty million dollars, let's say. And then you say you break it down the problem in two chunks and saying I'm going to look at a problem which will which I can take a data worse six months and if I can generate two percent of that savings, then it's a problem worth solving. So the concept that I say is that look at the data from a proof of value product, not proof of concept concept. is well known that data works, the data science works right, and the data that you have actually, how much value does it have? And then you start integrating data from many places, saying, for example, if it is a pump, you can connect whether data, you can connect, if possible, hr data, right, or connect your chemicals data, you can connect your deployment, repair, log parts, log whatever it is. you start connecting different data sets, saying or whenever we replace a bearing of this model, it fails. So you have to start asking questions from the data and then adding on more data sets. So you do think this craft best of fashion? Very yeah, so first phase should be less than six lessons, around sixteen weeks or less, on a very small amount of data where you show, yes, the data has some value. Then you add more data than you do another small project called proof of value. Then you say I need these five more data sets to connect to it. They could be under different silos, and then you scale the problem so by the third step you already know how much value are going to generate, either in cost savings or revenue generation or accuracy or efficiency or MPV. So it is a anything you do with data has always value. So were you know, you may have hard people saying on my digital project failed, on my data sience project fail. I don't believe in that at all because no projects fail because every project has a value. You could not scale it. that a different issue. Well, actually, some of the data that my company, a side that I remember and every bat companis actually the day to the data on the failure rate priority projects is appalling. It's civilians real is simplorked. About eighty percent of IT projects never make it past the Pfc. when you do a click on that, the prefect concept you when you double click on that. It's actually not really a technical problem. There are there are issues definitely to do...

...with the device. Most people don't know anything about how to design and don't want to know anything about how to design, and that's a gap that we that we filt working with the module and Eventualis, for instance, the quick terms of the world, which amount there's the world. But also it fails because they certainly it's like to an your tap on. They suddenly start collecting a lot of data and that's the point at which they just breeze. I mean they just they can't measure the quality of what they're getting. They don't know which date is important, which they is not important. They haven't got an architecture for what are they process at the edge? What? What data do they back or if they send it all back to head off is partickular. Haven't imagine viewing an oil field and rig or something, the amount of data and Terri Bytes, paeda bytes and we are the amount. You just can't afford to send it back to your corporate headquarters to crunch it. So you've got to do edge processing and they just they just didn't think about problems of a data architecture and big data turn insight when they started there their project. And so you do see what you're saying in the General Statistics on the industry, in that people they don't start off with trying to drill it down to let's just try and find out one particular problem with good chase down that will particular problem. Often they start off with a horizontal approach and say, let's collect data, promise many things as possible and then we'll work out what to do with our data. And that's where often they just breathed and they think, I'm not I don't know what to do. Is I'm collecting data, but I don't know, I don't know what I'm going to do. Yeah, absolutely, you know, but my philosophy has been very different. You know, we can address as big as of a problem they in today's Today's environment. When I iote sensors or any device that is generating the data, data can be in any format today's world. Come because compute is so cheap, right and and in principle, you could really put out very what I should call powerful machines at the edge as well, in a very small factor, so you don't have to really back hall everything. Yeah, right, whether it's cloud or edge or whatever you want to call it, found known and you can do that right. You can develop the algorithms or the models, whether it's scientific or augmented models and you can push them back to the field, where where the challenge comes is that we really can't make automated yet because we have to really test anything we do in the field and any complex industry you really have to test and validate. More often than anything, it's right. The models are models after all, and more validation is done, but the size of the data is not should not be a concern, because if that was a concerned then, as I said, it and native companies will not exist because the problem has been solidage. Is it a rating Fast Enos? It's it's not in here. Its just because the emagine, especially with clouds, the amage of processing this available is not a it's not a blockage in the process. Is what you're saying. A salute here. It's more about business problem that we want to solve and whether I need to really look at the data from one month or should I just only look at a one week, because depending on the worklow we are talking we could be really creating more value within one week's period of work of data and should be good enough for us. Light or something. We might have to look beyond it. So most of the things we want to eventually do in real time. And so that means you don't don't really have to process Betabytes of data all the time, because no, we are not. We are not. Any of these industry may be, say refinedies. If they were to deploy Fortyzero censers, maybe they will get a daytime terabytes. Because no, ioti sensors sense data in Giga Bytes. Yes, per day, I guess it.

Just think that they another hour to that. This is probably speaking to the personal does an I will ring. I heard and it was a perios and oil ring all it sees me. No, refine or could have ten million census in it. Now, after a feeling, that's a better four five euros. Just say, is that the air or to spokes to pay for you say so. I've been you know, these are numbers written by a lot of people. I'm not sure. Sure, I've never seen them before, like I've not been to a field, so I can't say that for sure. But what how do they calculate? And what does the device means? What does the sensor mean? Are they really internet lot of things devices, or there any devices? Right? So it by definition, anything that is connected becomes an Internet of a thing or Indian right but if you are, if you have pumps that are running and generating data which is collected by hand, that that's not Internet of a thing, but it is a data yeah, right, so these are when people write these kind of articles. I don't know how many of them are really counted. What is connected and what is not connected. Almost certainly nobody be worried the IOG business and we have customusy. We or guess, and I let me tell you, in terms of true IOT devices as opposed to something with a control and it's able to spit out data, which isn't Iret, but it says true I devices, it's probably the hundreds in practical terms today. I mean it's nowhere. There said it might be in the future with maybe with big when we start getting product for gene networks in these locutations. But Eve and then I think the word phrase Iot is being stretched to cover everything at Troy. That's not what we're talking about. In fact, is counted productive to think that that's what we're talking about. Absolutely and I think again it's a future of the web, when everything becomes digital. Maybe that is when we will get that kind of censors account of sensors and connectivity. But we are. No, we are close to that right in if you look at land as industry, they're talking about digital oil feels twenty five years ago or something and integrated reservoir management some thirty years ago. So what is integrated and what is did which digital oil field really exist? Right? Because Foundation when you say digital, I I feel if everything is connected and you are really you're doing real time automation, that's than it becomes really valid. But pieces of the puzzle are automated, no doubt about it, but we don't have it fully holistic automated digital oil fields. Well, the other thing people set it up that allows transitions to one of the final big subjects was getting you eat it about crazy is got twenty years ago. But let's just say by days good people would say. Well, you know, the answer to this is nothing to do with human as you were saying. People would say by now or in the future shortly, it's all going to be artificial intelligence. It's going to be machine. Machines will say go, the humans will let go, they'll stand back. We were worrying about what we're going to do for jobs, because it's all going to be brother. You know, the machines are going to beat the humans at the analysis. They can learn about the Poms breaking, they can learn about the resolution, they can go they can give you from reactive to proactive, preemptive. Mean that's what we they see in people's cause they see it. It will break, and so you take in Stemhech we fix and then it would be. That would be reactive and productively. Ali Go on saying it's going to break, that it's going to oil. So put oil in before breaks and pre empting the test where you get any cardoon get. It's like the icon. While you were sleep, software update downloaded. We fixed a whole bunch of issues you never even knew you had.

But don't worry, you'll never have them anyway. Have a nice day. So by now we were going to be in this world where, or at least entering into this world, the machines and take over. No, I know we spoke previously you and I can see it is one of the I never we spoke you. You have a you have your doubts where not of the world of Ai, but you INSETT me. You didn't even like the phrase ai because of the your experience or the expand on that little bit. Yeah. So, so artificial intelligence by itself as a subject has been there for fifty plus years, right, and if we look at even the applications of Algorithms that are developed, it has been used by oil and gas industry for last forty five years, whether it's neural network, whether it is regression. All right, it doesn't matter which algorithm you're talking about. The world change on the technology side and the computer side. An artificial intelligence is just a subject, right. It's like my analog G that I've always explain. We never say that we are eating chemistry or we are wearing chemistry. Right, we are all closed. Are made out of chemicals. Food is made out of chemicals. Some chemistry going on. Right, application of chemistry that we're talking about. So this is in the same way. It's an application of artificial intelligence, whether it's related to audio, whether it's related to video, whether it's related to data, whether it's related to text, text, right, that is what you're talking so there is nothing, there is no box called artificial intelligence. Pain. And when these articles come out saying artificial intelligence in test law, is that the same box? Can I put it on my computer for a far doing my statistical analysis. No, right, so it is not a thing and that's where the confusion is. But in aspective of that occreates a field and it's important field and it allows you to analyze things that human beings by themselves could not do right at scale. Repeated task that can be optimized, and even can eventually when self learning algorithms will be there more matured. Then maybe things can actually improve, like you can see those examples in robotics a little bit, whether robot can learn and things like that. But where are we? We are far away from it in terms of application. Maybe, maybe in some defense sector things that we don't know. Who knows what going on there, but in a practical in a practical world, that's no. We're close to it because it requires, it requires in principle all the tacit knowledge that is sitting in your head and all the people who are actually in the field right what to do when that is in people's and engineers had, after two thousand and thirty, forty, fifty years of experience, and it's the stupid and if we look at the history of knowledge management in the companies, we have never figured out a way to capture the tacit knowledge. Knowledge, yeah, and that doesn't knowledge is what is really what algorithms will need to really make a decision. So back to your example about the pumps, weters. Well, asking the engineer has been around bridge is stability. If I was going to investigate the plate on one area, what we today experience, engineer will say, you really want to have a look at these poms because they break three three months and it's a really big issue. That's it comes from the just side. Experience. That does in knowledge. The chances of the computer system saying that to you are probably pretty pretty low. And and then when they do present with the data, they need that tastit knowledge, that experience, the thing that we can't codify to actually interpret the data and prosise the actions. And so it's a company, you're saying, you guess it's a combination of the two. Absolutely very important combination, especially in the island gas industry. Having worked in like you know, and consulted in almost some and adverticals before I came here,...

...and I can tell you in Alan has industy the people have so much knowledge of because the processes are complex, irrespective of what section of the work work life sorry, work flow. We talk about in general the energy sector. If you look at it, it's a very verd you call science and engineering driven industry, and so a lot of these people have so much tacit knowledge in them that really needs to be captured and can be taken advantage of. For example, when you're drilling right, if you think of it, the person can feel and say I need to rotate this much the building. Yes, now to to get a algorithm to do that, you have to really look at so many things. First year to understand what is the force coming back easy, and then you are to really analyze what I did in the past. Few scenarios like that. So it will take some time and and that's where I'm saying that this you'd but at the on the other hand, those people who are experts, they have this sense that they feel this, see the sound or the field of vibration and they said do this now. They don't believe. The data, the data of people don't believe don't have the same knowledge as the engineer. So they have to come together and I feel that the people who have that tacit knowledge, they can be trained with the data knowledge, and that is what I call the talent transformation process, and I a sally, not the other way around, the other that round is hard because you you can't get the field experience and it. Yes, yeah, you can only get what the feeld people tell you. Yeah, but that, but the few people have so much, so you really need to throw them a laugh thing. This is what happening here. When do I do? And then you fortify that it was last question that, because it's opens, is as up. So may different questions. The combination of the field experience to give you the tested knowledge and the ability to make that instinctive human judgment that says this is right or this is wrong. The vibration. It feels right, doesn't feel right. We don't know where it comes from, but we can do it. And then the machine playing its role, analyzing things very, very quickly as well. Does that mean that in your role at Halliburton, do you train people? Do you recruit different types to people for the world that we're heading into? You know, do you look for certain types of degrees? And reminded listening to do not in the field of owing gas, but you know, conversation. Certainly, I've been involved in for many, many years lots of different industries that I've worked in, people saying, you know, NBA is NBA students are useless because they don't have any of the practical experience, but that they whereas cos the NBA schools, will think that they're the train of the future leaders and everyone's got all the knowledge they've ever did because they got an MBA. You know, you take that you've never applied it to an oil engineer. So and often, like in the case about those or those who went to work for several years when did an MBA and actually she felt at least she was much more valuable at the end of that that if she's done it the other way around. So do you in how you can get make recommendations in terms of what types of people you employ, given in this world that were in already and heading more into, or do you actually run internal training courses on have a hit this combination of the machine and the Tusit and the human working in harmony? Yeah, found answer all those parts in a interestingly so as an academic professor, all my students I for MBA especially. I tell them there's no point in doing MBA after bachelor's get right couple of years of experience. Then do an MBA. Then you will know what mistakes I did or what you didn't do right. She got it, and that then the value of MBA becomes really important. Otherwise it's like taking any other courts. You passed it and you're done right. And that's my first recommendation to most people in...

...terms of since in my background was not a direct vile and gas, so I know that it's all about generating value from the data and I think in that way. Most of the team that I built initially are all people from different fields of science or engineering or other areas. So I have a PhD in atomic physics, Slash Aster physics. I have a PhD in chemical engineering, mathematics, economics, things like that. So they can look, they can think totally differently, but then you pare them up with the task, a knowledge people that subject matter experts. That helps, and then over the years we actually developed our own training program not only for individual contributors but all the way to the leadership, because one is you have to really keep these people also in house. Right there's interesting challenges in the world and if especially a data science people, their high, high end demand really. It is our jobs out there offering future ranges exactly. So the way my philosophy has been that for all the data scientists, give them interesting problems. Don't give them and put them in a box and do just one problem. If they are doing multiple problems at the same time, there's no problem with that. In fact they lock because then they can think of it over that I have this kind of data or these other algorithms working, but I have this kind of data wise is not working. So they have their own compare and contrast going on within themselves and and then they're interacting with different domain experts, so to say, and that helps them really think beyond a simple problem and then they it's an exciting environmental work and that's how we have grown this center in Bangalore and in Ustern and Columbia and many other places. We are working with so many people. But the training part we've developed is because the same people who are actually working on a problem, they are actually teaching the House and House of this field to the domain experts. So they when they ask questions, they learn from the domain expert. Why are they asking this question? Why can't I find this and when the domain experts is how to do mathematically or why it is like this. They can explain it and that this synergy is significant. And last I think large. Just in last two years I think we've trained over thousand people in the industry and so and and hence I don't really worry about the talent pool side of it. In fact, I'm one of the hats I we are is the managing director of India Center and in the last one year I hired, of you, about hundred people from all different fields. So so you know, it's a fascinating area to work in and I think the potential is significant. As I say, the opportunities are significant because we are only scratch the surface of the of the industry. And if we really have the desire to build full implementation of IOTI sensors, properly, getting the fives network working or beyond five g working, which will reduce the cost to move the data and bring the speed to the connectivey to then I think we will have to build what is called a digital twin, of digital twins. And so there's a fascinating field and of course that T's a knowledge is not going anywhere. I do I call it augmented augmented analysis going on. Yeah, it's what. It's a fascinating story. It's a fascinating journey and it's also reflective, of some way of of Halliburton's journey as a company into moving more into data and data services for the clients and, as you say, all the efficiencies. And then the whole subject of digital twins is something that we do plan together as well in a future podcast book. For the moment, we better leave it there because we do it so...

...much, so much grand. So, Manya, I can just finished bride the subject, thanking you for your time and sharing with all listeners your journey, what you're doing in your thoughts on how to go about it, and for also astrailing those people who are have we're out there being concerned about whether the machines will take over the actually, you don't believe that they will and that we're all going to have plenty of do going forward in future years. So with that, I just want to say thanks to everyone for listening. You've listening to the IOT leaders podcast with me Your Personal Gil. If you have any feedback or questions on it, to remember that we do have an email address which is iot leaders at s I think that's e Se Yecom so we loved to hear from you and he suggests from many subjects that you would like us to cover. As you have this this is about guest we can actually go very broad or reading vertical into industry and we love to hear from you as to what you would like to have a discussion about or even when you feel you'd like to be a guest on the show. So let's beat it there. Such a thank you very much for your time and for our listeners. I will see you and talk to you on the next as. Thanks very thank you, nick my not. Thank you. Thanks for tuning in to Iot leaders, a podcast brought to you by SI. Our team delivers innovative Global Iot cellular connectivity solutions that just work, helping our customers deploy differentiated experiences and disrupt their markets. Learn more at SICOM. You've been listening to iote leaders, featuring digitization leadership on the front lines of Iot. Our Vision for this podcast is to be your guide to Iot and digital disruption, helping you to plot the right route to success. We hope today's lessons, stories, strategies and insights have changed your vision of Iot. Let us know how we're doing by subscribing, rating, reviewing and recommending us. Thanks for listening. Until next time,.

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